Re: [Idnet] IDN dedicated session call for case
Simone Ferlin <simone@ferlin.io> Wed, 09 August 2017 02:30 UTC
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From: Simone Ferlin <simone@ferlin.io>
Date: Wed, 09 Aug 2017 11:28:40 +0900
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To: Stenio Fernandes <sflf@cin.ufpe.br>
Cc: Jérôme François <jerome.francois@inria.fr>, yanshen <yanshen@huawei.com>, "idnet@ietf.org" <idnet@ietf.org>, Albert Cabellos <albert.cabellos@gmail.com>, "Diego R. Lopez" <diego.r.lopez@telefonica.com>
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Subject: Re: [Idnet] IDN dedicated session call for case
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Dear Jerome, Very interesting use-case, +1 support. I have interest in such activities for traffic classification, anomaly detection in particular for encrypted traffic. > On Wed, Aug 9, 2017 at 12:20 AM, Stenio Fernandes <sflf@cin.ufpe.br> wrote: >> Hi Jerome, Diego, et al, >> >> Those are excellent use cases. I have some published work on applied >> machine learning to computer networking problems, including flow-based >> traffic classification. I think another use case would be applying >> unsupervised learning techniques for anomaly detection. I can >> elaborate further on this. >> >> Stenio >> >> On Tue, Aug 8, 2017 at 10:59 AM, Jérôme François >> <jerome.francois@inria.fr> wrote: >>> 100% agree with you. I was far from being exhaustive as traffic features may >>> depend on types of traffic (kin of sub use cases) >>> >>> jerome >>> >>> Le 08/08/2017 à 16:56, Diego R. Lopez a écrit : >>> >>> Hi Jerome, >>> >>> >>> >>> Agreed. This is a use case we are very much interested in, and actually >>> working in it now. Just let me say we are trying to evaluate which are the >>> significant features of the flow to perform a proper classification, >>> depending on the flow nature (TLS, DTLS, QUIC, IPsec…), and that would >>> define the concrete data to be exchanged or stored. >>> >>> >>> >>> Be goode, >>> >>> >>> >>> -- >>> >>> "Esta vez no fallaremos, Doctor Infierno" >>> >>> >>> >>> Dr Diego R. Lopez >>> >>> Telefonica I+D >>> >>> http://people.tid.es/diego.lopez/ >>> >>> >>> >>> e-mail: diego.r.lopez@telefonica.com >>> >>> Tel: +34 913 129 041 >>> >>> Mobile: +34 682 051 091 >>> >>> ----------------------------------- >>> >>> >>> >>> On 8/8/2017, 16:49 , "IDNET on behalf of Jérôme François" >>> <idnet-bounces@ietf.org on behalf of jerome.francois@inria.fr> wrote: >>> >>> >>> >>> Hi all, >>> >>> Here is another use case about traffic classification. >>> >>> Use case N+3: (encrypted) traffic classification >>> >>> Description: collect flow-level traffic metrics such as protocol >>> information but also meta metrics such as distribution of packet sizes, >>> inter-arrival times... Then use such information to label the trafic with >>> the underlying application assuming that the granularity of classification >>> may vary (type of application, exact application name, version...) >>> Process: 1. collect packet information 2. flow reassembly (using >>> directly flow format such as IPFIX might be possible but depends on the type >>> of traffic, e.g. extracting the TLS application data is useful for encrypted >>> traffic) 3. Collect application specific information (useful when targeting >>> a single type of application) = out of network information 4. train the >>> model 5. Online or offline testing 4. Apply application level policies. >>> Data Format: Time : [Start, End, Unit, Number of Value, Sampling >>> Period] >>> Position: [Device ID, Port ID] >>> Direction: IN / OUT >>> Flow level metric: packet size >>> distributions, number of packets, inter-arrival time distribution, >>> (+ application specific knowledge : payload >>> parsing) >>> >>> Message : Request: ask for the data >>> Reply: Data >>> Notice: For notification or others >>> Policy: Control policy >>> >>> >>> Best regards, >>> jerome >>> >>> >>> Le 08/08/2017 à 06:52, Albert Cabellos a écrit : >>> >>> Hi all >>> >>> >>> >>> Here´s another use-case: >>> >>> >>> >>> Use case N+2: QoE >>> Description: Collect low-level metrics (SNR, latency, jitter, >>> losses, etc) and measure QoE. Then use ML to understand what is the relation >>> between satisfactory QoE and the low-level metrics. As an example learn that >>> when delay>N then QoE is degraded, but when M<delay<N then QoE is >>> satisfactory for the customers (please note that QoE cannot be measured >>> directly over your network). This is useful to understand how the network >>> must be operated to provide satisfactory QoE. >>> Process: 1. Low-level data collection and QoE measurement ; 2. >>> Training Model (input low-level metrics, output QoE); 3. Real-time data >>> capture and input; 4. Predict QoE; 5. Operate network to meet target QoE >>> requirement, go to 3. >>> Data Format: Time : [Start, End, Unit, Number of Value, Sampling >>> Period] >>> Position: [Device ID, Port ID] >>> Direction: IN / OUT >>> Low-level metric : SNR, Delay, Jitter, >>> queue-size, etc >>> >>> >>> Message : Request: ask for the data >>> Reply: Data >>> Notice: For notification or others >>> Policy: Control policy >>> >>> >>> >>> Kind regards >>> >>> >>> >>> Albert >>> >>> >>> >>> On Wed, Aug 2, 2017 at 7:12 PM, yanshen <yanshen@huawei.com> wrote: >>> >>> Dear all, >>> >>> Since we plan to organize a dedicated session in NMRG, IETF100, for applying >>> AI into network management (NM), I’d try to list some Use Cases and propose >>> a roadmap and ToC before Nov. >>> >>> These might be rough. You are welcome to refine them and propose your >>> focused use cases or ideas. >>> >>> Use case 1: Traffic Prediction >>> Description: Collect the history traffic data and external data >>> which may influence the traffic. Predict the traffic in short/long/specific >>> term. Avoid the congestion or risk in previously. >>> Process: 1. Data collection (e.g. traffic sample of physical/logical >>> port ); 2. Training Model; 3. Real-time data capture and input; 4. >>> Predication output; 5. Fix error and go back to 3. >>> Data Format: Time : [Start, End, Unit, Number of Value, Sampling >>> Period] >>> Position: [Device ID, Port ID] >>> Direction: IN / OUT >>> Route : [R1, R2, ..., RN] (might be useful >>> for some scenarios) >>> Service : [Service ID, Priority, ...] (Not >>> clear how to use it but seems useful) >>> Traffic: [T0, T1, T2, ..., TN] >>> Message : Request: ask for the data >>> Reply: Data >>> Notice: For notification or others >>> Policy: Control policy >>> >>> Use case 2: QoS Management >>> Description: Use multiple paths to distribute the traffic flows. >>> Adjust the percentages. Avoid congestion and ensure QoS. >>> Process: 1. Data capture (e.g. traffic sample of physical/logical >>> port ); 2. Training Model; 3. Real-time data capture and input; 4. Output >>> percentages; 5. Fix error and go back to 3. >>> Data Format: Time : [Timestamp, Value type (Delay/Packet >>> Loss/...), Unit, Number of Value, Sampling Period] >>> Position: [Link ID, Device ID] >>> Value: [V0, V1, V2, ..., VN] >>> Message : Request: ask for the data >>> Reply: Data >>> Notice: For notification or others >>> Policy: Control policy >>> >>> Use case N: Waiting for your Ideas >>> >>> Also I suggest a roadmap before Nov if possible. >>> >>> ### Roadmap ### >>> Aug. : Collecting the use cases (related with NM). Rough thoughts and >>> requirements >>> Sep. : Refining the cases and abstract the common elements >>> Oct. : Deeply analysis. Especially on Data Format, control flow, or other >>> key points >>> Nov.: F2F discussions on IETF100 >>> ### Roadmap End ### >>> >>> A rough ToC is listed in following. We may take it as a scope before Nov. >>> Hope that the content could become the draft of draft. >>> >>> ###Table of Content### >>> 1. Gap and Requirement Analysis >>> 1.1 Network Management requirement >>> 1.2 TBD >>> 2. Use Cases >>> 2.1 Traffic Prediction >>> 2.2 QoS Management >>> 3.3 TBD >>> 3. Data Focus >>> 3.1 Data attribute >>> 3.2 Data format >>> 3.3 TBD >>> 4. Aims >>> 4.1 Benchmarking Framework >>> 4.2 TBD >>> ###ToC End### >>> >>> >>> Yansen >>> >>> _______________________________________________ >>> IDNET mailing list >>> IDNET@ietf.org >>> https://www.ietf.org/mailman/listinfo/idnet >>> >>> >>> >>> >>> >>> >>> _______________________________________________ >>> >>> IDNET mailing list >>> >>> IDNET@ietf.org >>> >>> https://www.ietf.org/mailman/listinfo/idnet >>> >>> >>> >>> >>> ________________________________ >>> >>> Este mensaje y sus adjuntos se dirigen exclusivamente a su destinatario, >>> puede contener información privilegiada o confidencial y es para uso >>> exclusivo de la persona o entidad de destino. Si no es usted. el >>> destinatario indicado, queda notificado de que la lectura, utilización, >>> divulgación y/o copia sin autorización puede estar prohibida en virtud de la >>> legislación vigente. Si ha recibido este mensaje por error, le rogamos que >>> nos lo comunique inmediatamente por esta misma vía y proceda a su >>> destrucción. >>> >>> The information contained in this transmission is privileged and >>> confidential information intended only for the use of the individual or >>> entity named above. If the reader of this message is not the intended >>> recipient, you are hereby notified that any dissemination, distribution or >>> copying of this communication is strictly prohibited. If you have received >>> this transmission in error, do not read it. Please immediately reply to the >>> sender that you have received this communication in error and then delete >>> it. >>> >>> Esta mensagem e seus anexos se dirigem exclusivamente ao seu destinatário, >>> pode conter informação privilegiada ou confidencial e é para uso exclusivo >>> da pessoa ou entidade de destino. Se não é vossa senhoria o destinatário >>> indicado, fica notificado de que a leitura, utilização, divulgação e/ou >>> cópia sem autorização pode estar proibida em virtude da legislação vigente. >>> Se recebeu esta mensagem por erro, rogamos-lhe que nos o comunique >>> imediatamente por esta mesma via e proceda a sua destruição >>> >>> >>> >>> _______________________________________________ >>> IDNET mailing list >>> IDNET@ietf.org >>> https://www.ietf.org/mailman/listinfo/idnet >>> >> >> >> >> -- >> Prof. Stenio Fernandes >> CIn/UFPE >> http://www.steniofernandes.com >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet
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